MCIC Wooster, OSU
2024-01-25
The shorthand sequencing, like in “high-throughput sequencing” in the title of this presentation, generally refers to determining the nucleotide sequence of fragments of DNA.
What about RNA or proteins?
RNA is usually reverse transcribed to DNA (cDNA) prior to sequencing, as in nearly all “RNA-seq”.
Direct RNA sequencing is possible with one of the sequencing technologies we’ll discuss, but this is under development and not yet widely used.
High-throughput sequencing (HTS)
Sequences 105-109, usually randomly selected, DNA fragments (“reads”) at a time — two types:
High-throughput sequencing (HTS)
Sequences 105-109, usually randomly selected, DNA fragments (“reads”) at a time — two types:
Modified after Pereira et al. 2020
Modified after Pereira et al. 2020
Sequences a single, typically PCR-amplified, short-ish (≤900 bp) DNA fragment at a time.
Sequencing is performed by synthesizing a new DNA strand in part with fluorescently-labeled nucleotides — a different color for each base (A, C, G, T).
The final result is a chromatogram that can be base-called:
The entire human genome (3 Gbp) was sequenced with Sanger technology!
Anyone want to hazard a guess how much this cost, approximately?
https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost
With the advent of NGS, Sanger sequencing has become much less common but is not obsolete.
Some present-day applications of Sanger sequencing include:
Examining variation among individuals or populations in one or more candidate or marker genes (for population genetics, phylogenetics, functional inferences, etc.)
Taxonomic identification of samples
Variant analysis (for population genetics/genomics, molecular evolution, GWAS, etc.):
Whole-genome “resequencing”
Reduced-representation libraries (e.g. RADseq, GBS)
RNA-seq (transcriptome analysis)
Other functional sequencing methods like methylation sequencing, ChIP-seq, etc.
Microbial community characterization
Metabarcoding
Shotgun metagenomics
Short-read (Illumina) HTS: 50-300 bp reads
Long-read HTS: longer & more variable read lengths (PacBio: 10-50 kbp, ONT: 10-100 kbp)
Genome assembly
Haplotype and large structural variant calling
Transcript isoform identification
Taxonomic identification of single reads (microbial metabarcoding)
SNP variant analysis
Read-as-a-tag: the goal is just to know a read’s origin in a reference genome, like in counting applications such as RNA-seq
Currently, no sequencing technology is error-free, and several types of errors can occur:
Base call errors, e.g. a base that was called as an A may instead be a G.
Insertion or deletion (indel) errors
When the base calling software is not confident at all, it can also return Ns (= undetermined).
Quality scores in sequence data
When you get sequences from a high-throughput sequencer, base calls have typically already been made. Every base is also accompanied by a quality score (inversely related to the estimated error probability).
But overcoming sequencing errors is made more challenging by natural genetic variation among and within (heterozygosity due to diploid genomes) individuals
Typical depths of coverage: at least 50-100x for genome assembly; 10-30x for resequencing.
100-300 bp reads with 0.1-0.2% error rates
More reads, lower per-base cost, and lower error rates than long-read sequencing1.
In a sequencing context, a “library” is a collection of nucleic acid fragments ready for sequencing.
In Illumina and other HTS libraries, these fragments number in the millions or billions and are often randomly generated from input such as genomic DNA:
This procedure is called library prep, and is typically done for you by a sequencing facility or company.
Different library prep procedures are used depending on the type of sequencing (WGS, RAD-seq, RNA-seq, etc.) and HTS technology — and some include more specific fragment generation or selection.
We’ll see the specific library prep steps for RNA-seq next week.
After library prep (here, for Illumina sequencing), each DNA fragment is flanked by several types of short sequences that together make up the “adapters”:
Adapter components?
After talking about paired-end vs. single-end sequencing and the way Illumina sequencing works, we’ll take a closer look at the individual components of adapters.
In Illumina sequencing, DNA fragments can be sequenced from both ends as shown below —
this is called “paired-end” (PE) sequencing:
When sequencing is instead single-end (SE), no reverse read is produced:
Paired-end sequencing is a way to effectively increase the read length. (In the resulting sequence files, the two reads in each pair are separate, but they can be matched thanks to shared read IDs.)
Earlier, we saw that the maximum read length of Illumina is 300 bp but in paired-end sequencing, this becomes “2 x 300 bp”, etc.
Insert size varies — because the library prep protocol can aim for various sizes, and because of variation due to limited precision in size selection. In some case, the insert size can be:
First, library fragments bind to a surface thanks to the adapters, and the DNA templates (the biological sequences) are then PCR-amplified to form “clusters” of identical fragments:
In the diagram above, for illustrative purposes:
Only a few nucleotides are shown (1 block = 1 nucleotide) — in reality, fragments are much longer
Only two templates => clusters are shown — in reality, there are millions
Then, sequencing is performed by synthesizing a new strand using fluorescently-labeled bases and taking a picture each time a new nucleotide is incorporated:
The different templates within a cluster get out of sync because occasionally:
They miss a base incorporation
They incorporate two bases at once
This error profile is why, for Illumina:
There are hard limits on read lengths
Base quality scores typically decrease along the read
Now that you have a better idea of how Illumina sequencing works, let’s briefly revisit the adapters flanking the DNA, and see their different components:
Modified after http://nextgen.mgh.harvard.edu/IlluminaChemistry.html
Now that you have a better idea of how Illumina sequencing works, let’s briefly revisit the adapters flanking the DNA, and see their different components:
Modified after http://nextgen.mgh.harvard.edu/IlluminaChemistry.html
Multiplexing!
Using the indices/barcodes in adapters, up to 96 samples can be multiplexed into a single library.
The technologies underlying the two main long-read HTS technologies are very different, but have some commonalities beyond long reads — they:
Error rates are changing
As a shorthand that was universally true until recently, I mentioned earlier that long-read HTS has higher error rate than short-read (Illumina) HTS.
However, error rates in one type of PacBio sequencing where individual fragments are sequenced multiple times (“HiFi”) are now lower than in Illumina.
A single strand of DNA passes through a nanopore —
the electrical current is measured, which depends on the combination of bases passes in the pore:
https://www.genome.gov/genetics-glossary/Nanopore-DNA-Sequencing
See also this short video: https://www.youtube.com/watch?v=RcP85JHLmnI
Under development!
ONT constantly releases new flow cells with updated technology, which have led to large decreases in error rates over the past decade — and even over the past two or so years.
There is also a lot of development in ONT base-calling software so it is useful to receive and keep pre-basecall files: re-basecalling a few years later with updated software can make a difference.
Advantages of ONT:
Low capital cost, portability (in-the-field sequencing!)
Read length not inherently limited, some extremely long reads
Lower cost per base
Can sequence RNA directly (but still under development)
Disadvantages of ONT:
Higher error rates
Some systematic errors (e.g. homopolymers)
As methods facilitating genomics and transcriptomics research, genomes loom large in HTS. Specifically, most HTS applications either require a “reference genome” or involve its production.
What exactly does “reference genome” refer to? We’ll discuss three components to this phrase:
Taxonomic identity
Typically considered at the species level, so then it should involve the focal species. But:
If necessary, it is often possible to work with reference genomes of closely related species
Conversely, multiple reference genomes may exist, e.g. for different subspecies/populations
https://en.wikipedi.org/wiki/Karyotype
Key features:
Number of distinct chromosomes
Ploidy
With increasing usage & quality of long-read HTS, we are generating better assemblies
For chromosome-level assemblies, i.e. with one contiguous sequence for each chromosome, additional technologies than sequencing are often needed (e.g. Hi-C, optical mapping)
Many assemblies are not “chromosome-level”, but consist of –often 1000s of– contigs and scaffolds.
Even chromosome-level assemblies are not 100% complete (and contain “unplaced” scaffolds)
Contigs are contiguous, known stretches of DNA created by the assembly process, basically by overlapping reads.
Often, the order and orientation of two or more contigs is known, but there is a gap of unknown size between them. Such contigs are connected into scaffolds with a stretch of Ns in between.
Annotating a genome consists of two main steps:
Structural annotation
The identification of genes and other genomic features within the genome sequence
Functional annotation
Giving names & assigning functions to (mostly) genes
Genome annotation heavily relies on information from other organisms’ genomes, lifting over annotations based on the concept of sequence homology.
How is this data stored?
Both genome assemblies and annotations are typically saved in a single text file each — more on that soon.
All common genetic/genomic data files are plain-text, meaning that they can be opened by any text editor. However, they are often compressed to save space. The main types are:
FASTQ
The standard format for HTS reads — contains a quality score for each nucleotide.
SAM/BAM
An alignment format for HTS reads
FASTA files contain one or more (sometimes called multi-FASTA) DNA or amino acid sequences, with no limits on the number of sequences or the sequence lengths.
As mentioned, they are versatile, and are the standard format for:
Genome assembly sequences
Transcriptomes and proteomes (all of an organism’s transcripts & amino acid sequences, resp.)
Sequence downloads from NCBI such as a single gene/protein or other GenBank entry
Sequence alignments (but not from HTS reads)
The following example FASTA file contains two entries:
>unique_sequence_ID Optional description
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAAAA
>unique_sequence_ID2
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAATGEach entry contains a header and the sequence itself, and:
> and are otherwise “free form” but usually provide an identifier (and sometimes metadata) for the sequenceFASTA file name extensions are variable:
Generic extensions are .fasta and .fa
Also used are extensions that explicitly indicate whether sequences are nucleotide (.fna) or amino acids (.faa)
FASTQ is the standard format for HTS reads.
Each read forms one FASTQ entry and is represented by four lines, which contain, respectively:
@ and e.g. uniquely identifies the read+ (plus sign)The quality scores we saw in the read on the previous slide represent an estimate of the error probability of the base call.
Specifically, they correspond to a numeric “Phred” quality score (Q), which is a function of the estimated probability that a base call is erroneous (P):
Q = -10 * log10(P)
For some specific probabilities and their rough qualitative interpretation for Illumina data:
| Phred quality score | Error probability | Rough interpretation |
|---|---|---|
| 10 | 1 in 10 | terrible |
| 20 | 1 in 100 | bad |
| 30 | 1 in 1,000 | good |
| 40 | 1 in 10,000 | excellent |
This numeric quality score is represented in FASTQ files not by the number itself, but by a corresponding “ASCII character”.
This allows for a single-character representation of each possible score — as a consequence, each quality score character can conveniently correspond to (& line up with) a base character in the read.
| Phred quality score | Error probability | ASCII character |
|---|---|---|
| 10 | 1 in 10 | + |
| 20 | 1 in 100 | 5 |
| 30 | 1 in 1,000 | ? |
| 40 | 1 in 10,000 | I |
A rule of thumb
In practice, you almost never have to manually check the quality scores of bases in FASTQ files, but if you do, a rule of thumb is that letter characters are good (Phred of 32 and up).
FASTQ files have no size limit, so you may receive a single file per sample, although:
With paired-end (PE) sequencing, forward and reverse reads are split into two files:
forward reads contain R1 and reverse reads contain R2 in the file name.
If sequencing was done on multiple lanes, you get one (SE) or two (PE) files per lane per sample.1
FASTQ files have the extension .fastq or .fq (but are commonly compressed, leading to fastq.gz etc.). All in all, having paired-end FASTQ files for 2 samples could look like this:
The GTF and GFF formats are tab-delimited tabular files that contain genome annotations, with:
One row for each annotated “genomic feature” (gene, exon, etc.)
One column for each piece of information about a feature, like its genomic coordinates
See the sample below, with an added header line (not normally present) with column names:
seqname source feature start end score strand frame attributes
NC_000001 RefSeq gene 11874 14409 . + . gene_id "DDX11L1"; transcript_id ""; db_xref "GeneID:100287102"; db_xref "HGNC:HGNC:37102"; description "DEAD/H-box helicase 11 like 1 (pseudogene)"; gbkey "Gene"; gene "DDX11L1"; gene_biotype "transcribed_pseudogene"; pseudo "true";
NC_000001 RefSeq exon 11874 12227 . + . gene_id "DDX11L1"; transcript_id "NR_046018.2"; db_xref "GeneID:100287102"; gene "DDX11L1"; product "DEAD/H-box helicase 11 like 1 (pseudogene)"; pseudo "true"; Some details on the more important/interesting columns:
+ (forward) or - (reverse) strandUsing specialized bioinformatics tools, you can align HTS reads (in FASTQ files) to a reference genome assembly (in a FASTA file).
The resulting alignments are stored in the SAM (uncompressed) / BAM (compressed) format.
SAM/BAM are tabular files with one line per alignment, each of which includes:
The position in the genome that the read aligned to
A mapping score based on the length of the alignment and the number of mismatches
The sequence of aligned the read itself
File conversions
FASTQ files can be converted to FASTA files (losing quality information) but not vice versa
SAM/BAM files can be converted to FASTQ files (losing alignment information) but not vice versa
Proteome FASTA files can be produced from the combination of a FASTA genome assembly and a GFF/GTF genome annotation